

import argparse

import fairseq
import torch
from torch import nn

from transformers import (
    MBartaaTokenizer,
    MBartConfig,
    MBartForCausalLM,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
lowercase			:				Tuple   =     logging.get_logger(__name__)

lowercase			:				Optional[Any]   =     {
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}
lowercase			:				Any   =     [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_		):
							for attribute in key.split('''.'''		):
														A      :					List[Any]      		=					getattr(lowerCamelCase_     ,				lowerCamelCase_		)

							if weight_type is not None:
														A      :					Dict      		=					getattr(lowerCamelCase_     ,				lowerCamelCase_		).shape
							else:
														A      :					Any      		=					hf_pointer.shape

							assert hf_shape == value.shape, (
							    F'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
							    F' {value.shape} for {full_name}'
							)

							if weight_type == "weight":
														A      :					Tuple      		=					value
							elif weight_type == "weight_g":
														A      :					Optional[Any]      		=					value
							elif weight_type == "weight_v":
														A      :					Optional[int]      		=					value
							elif weight_type == "bias":
														A      :					Optional[int]      		=					value
							else:
														A      :					int      		=					value

							logger.info(F'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'		)






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_		):
							A      :					int      		=					[]
							A      :					Union[str, Any]      		=					fairseq_model.state_dict()

							A      :					Optional[Any]      		=					hf_model.feature_extractor
							A      :					Any      		=					hf_model.adapter

							for name, value in fairseq_dict.items():
														A      :					Any      		=					False
														if "conv_layers" in name:
																					load_conv_layer(
																					    lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				hf_model.config.feat_extract_norm == '''group'''     ,				)
																					A      :					Dict      		=					True
														elif any(x in name for x in ['''adaptor''', '''w2v_encoder.proj.''', '''w2v_proj_ln.''']		):
																					load_adapter(lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_		)
																					A      :					Union[str, Any]      		=					True
														else:
																					for key, mapped_key in MAPPING.items():
																												if key in name or key.split('''w2v_model.'''		)[-1] == name.split('''.'''		)[0]:
																																			A      :					List[str]      		=					True
																																			if "*" in mapped_key:
																																										A      :					Any      		=					name.split(lowerCamelCase_		)[0].split('''.'''		)[-2]
																																										A      :					List[str]      		=					mapped_key.replace('''*'''     ,				lowerCamelCase_		)
																																			if "weight_g" in name:
																																										A      :					Union[str, Any]      		=					'''weight_g'''
																																			elif "weight_v" in name:
																																										A      :					Union[str, Any]      		=					'''weight_v'''
																																			elif "bias" in name:
																																										A      :					Optional[Any]      		=					'''bias'''
																																			elif "weight" in name:
																																										A      :					Optional[int]      		=					'''weight'''
																																			else:
																																										A      :					Any      		=					None
																																			set_recursively(lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_		)
																												continue
														if not is_used:
																					unused_weights.append(lowerCamelCase_		)

							logger.warning(F'Unused weights: {unused_weights}'		)






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_		):
							A      :					Optional[Any]      		=					full_name.split('''conv_layers.'''		)[-1]
							A      :					Union[str, Any]      		=					name.split('''.'''		)
							A      :					Optional[Any]      		=					int(items[0]		)
							A      :					Any      		=					int(items[1]		)

							if type_id == 0:
														if "bias" in name:
																					assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
																					    F'{full_name} has size {value.shape}, but'
																					    F' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'
																					)
																					A      :					Optional[int]      		=					value
																					logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.'		)
														elif "weight" in name:
																					assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
																					    F'{full_name} has size {value.shape}, but'
																					    F' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'
																					)
																					A      :					str      		=					value
																					logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.'		)
							elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
														if "bias" in name:
																					assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
																					    F'{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'
																					    " found."
																					)
																					A      :					int      		=					value
																					logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'		)
														elif "weight" in name:
																					assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
																					    F'{full_name} has size {value.shape}, but'
																					    F' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'
																					)
																					A      :					Optional[Any]      		=					value
																					logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'		)
							else:
														unused_weights.append(lowerCamelCase_		)






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_		):
							A      :					List[str]      		=					full_name.split('''adaptor.'''		)[-1]
							A      :					List[str]      		=					name.split('''.'''		)

							if items[1].isdigit():
														A      :					Any      		=					int(items[1]		)
							else:
														A      :					Any      		=					None

							if "adaptor" not in full_name:
														if "proj_ln" in full_name:
																					# has to be layer norm
																					if "bias" in name:
																												assert (
																												    value.shape == adapter.proj_layer_norm.bias.data.shape
																												), F'{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found.'
																												A      :					Optional[int]      		=					value
																												logger.info(F'Adapter proj layer norm bias was initialized from {full_name}.'		)
																					if "weight" in name:
																												assert (
																												    value.shape == adapter.proj_layer_norm.weight.data.shape
																												), F'{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found.'
																												A      :					Optional[Any]      		=					value
														else:
																					# has to be projection layer
																					if "bias" in name:
																												assert (
																												    value.shape == adapter.proj.bias.data.shape
																												), F'{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found.'
																												A      :					List[str]      		=					value
																												logger.info(F'Adapter proj layer bias was initialized from {full_name}.'		)
																					if "weight" in name:
																												assert (
																												    value.shape == adapter.proj.weight.data.shape
																												), F'{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found.'
																												A      :					Dict      		=					value
																												logger.info(F'Adapter proj layer weight was initialized from {full_name}.'		)
							elif isinstance(lowerCamelCase_     ,				lowerCamelCase_		):
														if "bias" in name:
																					assert (
																					    value.shape == adapter.layers[layer_id].conv.bias.data.shape
																					), F'{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found.'
																					A      :					Dict      		=					value
																					logger.info(F'Adapter layer {layer_id} bias was initialized from {full_name}.'		)
														elif "weight" in name:
																					assert (
																					    value.shape == adapter.layers[layer_id].conv.weight.data.shape
																					), F'{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found.'
																					A      :					str      		=					value
																					logger.info(F'Adapter layer {layer_id} bias was initialized from {full_name}.'		)
							else:
														unused_weights.append(lowerCamelCase_		)






def        snake_case__		(    lowerCamelCase_		):
							A		,		A      :					Tuple      		=					emb.weight.shape
							A      :					Any      		=					nn.Linear(lowerCamelCase_     ,				lowerCamelCase_     ,				bias=lowerCamelCase_		)
							A      :					Optional[Any]      		=					emb.weight.data
							return lin_layer






@torch.no_grad()
def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				):
							A      :					Tuple      		=					WavaVecaConfig.from_pretrained(
							    lowerCamelCase_     ,				add_adapter=lowerCamelCase_     ,				adapter_stride=lowerCamelCase_     ,				adapter_kernel_size=lowerCamelCase_     ,				use_auth_token=lowerCamelCase_     ,				output_hidden_size=lowerCamelCase_     ,				)
							A      :					List[Any]      		=					MBartConfig.from_pretrained(lowerCamelCase_		)

							# load model
							A		,		A		,		A      :					Optional[Any]      		=					fairseq.checkpoint_utils.load_model_ensemble_and_task(
							    [checkpoint_path]     ,				arg_overrides={
							        '''config_yaml''': config_yaml_path,
							        '''data''': '''/'''.join(dict_path.split('''/'''		)[:-1]		),
							        '''w2v_path''': checkpoint_path,
							        '''load_pretrained_decoder_from''': None,
							    }     ,				)
							A      :					List[Any]      		=					model[0].eval()

							# load feature extractor
							A      :					Union[str, Any]      		=					WavaVecaFeatureExtractor.from_pretrained(lowerCamelCase_     ,				use_auth_token=lowerCamelCase_		)

							# set weights for wav2vec2 encoder
							A      :					Dict      		=					WavaVecaModel(lowerCamelCase_		)

							recursively_load_weights_wavaveca(model.encoder     ,				lowerCamelCase_		)

							# load decoder weights
							A      :					Tuple      		=					MBartForCausalLM(lowerCamelCase_		)
							A		,		A      :					Tuple      		=					hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()     ,				strict=lowerCamelCase_		)
							logger.warning(F'The following keys are missing when loading the decoder weights: {missing_keys}'		)
							logger.warning(F'The following keys are unexpected when loading the decoder weights: {unexpected_keys}'		)

							A      :					List[str]      		=					SpeechEncoderDecoderModel(encoder=lowerCamelCase_     ,				decoder=lowerCamelCase_		)
							A      :					Optional[Any]      		=					False

							A      :					Tuple      		=					MBartaaTokenizer(lowerCamelCase_		)
							tokenizer.save_pretrained(lowerCamelCase_		)

							A      :					List[str]      		=					hf_wavavec.config.to_dict()
							A      :					Tuple      		=					tokenizer.pad_token_id
							A      :					Tuple      		=					tokenizer.bos_token_id
							A      :					Any      		=					tokenizer.eos_token_id
							A      :					str      		=					'''mbart50'''
							A      :					Union[str, Any]      		=					'''wav2vec2'''

							A      :					Union[str, Any]      		=					tokenizer.eos_token_id
							A      :					Optional[Any]      		=					250004
							A      :					List[str]      		=					tokenizer.eos_token_id

							A      :					Tuple      		=					SpeechEncoderDecoderConfig.from_dict(lowerCamelCase_		)

							hf_wavavec.save_pretrained(lowerCamelCase_		)
							feature_extractor.save_pretrained(lowerCamelCase_		)


if __name__ == "__main__":
		lowercase			:				Optional[Any]   =     argparse.ArgumentParser()
		parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
		parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
		parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
		parser.add_argument("--config_yaml_path", default=None, type=str, help="Path to yaml file of fine-tuned model")
		parser.add_argument(
		    "--encoder_config_path",
		    default="facebook/wav2vec2-xls-r-1b",
		    type=str,
		    help="Path to hf encoder wav2vec2 checkpoint config",
		)
		parser.add_argument(
		    "--decoder_config_path",
		    default="facebook/mbart-large-50-one-to-many-mmt",
		    type=str,
		    help="Path to hf decoder checkpoint config",
		)
		parser.add_argument("--add_adapter", default=True, type=bool, help="whethere to add model adapter layers")
		parser.add_argument("--adapter_stride", default=2, type=int, help="stride of adapter layers")
		parser.add_argument("--adapter_kernel_size", default=3, type=int, help="kernel size of adapter layers")
		parser.add_argument("--encoder_output_dim", default=10_24, type=int, help="encoder output dim")
		parser.add_argument("--start_token_id", default=25_00_04, type=int, help="`decoder_start_token_id` of model config")

		lowercase			:				str   =     parser.parse_args()
		convert_wavaveca_checkpoint(
		    args.checkpoint_path,
		    args.pytorch_dump_folder_path,
		    args.dict_path,
		    args.config_yaml_path,
		    encoder_config_path=args.encoder_config_path,
		    decoder_config_path=args.decoder_config_path,
		    add_adapter=args.add_adapter,
		    adapter_kernel_size=args.adapter_kernel_size,
		    adapter_stride=args.adapter_stride,
		    decoder_start_token_id=args.start_token_id,
		    encoder_output_dim=args.encoder_output_dim,
		)









import string
from math import logaa






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_		):
							A      :					List[str]      		=					document.translate(
							    str.maketrans(''''''     ,				''''''     ,				string.punctuation		)		).replace('''\n'''     ,				''''''		)
							A      :					Optional[int]      		=					document_without_punctuation.split(''' '''		)  # word tokenization
							return len([word for word in tokenize_document if word.lower() == term.lower()]		)






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_		):
							A      :					Dict      		=					corpus.lower().translate(
							    str.maketrans(''''''     ,				''''''     ,				string.punctuation		)		)  # strip all punctuation and replace it with ''
							A      :					Dict      		=					corpus_without_punctuation.split('''\n'''		)
							A      :					List[Any]      		=					term.lower()
							return (len([doc for doc in docs if term in doc]		), len(lowerCamelCase_		))






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_=False		):
							if smoothing:
														if n == 0:
																					raise ValueError('''log10(0) is undefined.'''		)
														return round(1 + logaa(n / (1 + df)		)     ,				3		)

							if df == 0:
														raise ZeroDivisionError('''df must be > 0'''		)
							elif n == 0:
														raise ValueError('''log10(0) is undefined.'''		)
							return round(logaa(n / df		)     ,				3		)






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_		):
							return round(tf * idf     ,				3		)






